Spatial income convergence among and within the Indian states

Manish Chauhan, Somesh K. Mathur and Praveen Kulshreshtha

Indian Growth and Development Review, 2026, vol. 19, issue 2-3, 289-324

Abstract: PurposeThis paper aims to decipher the spatial income convergence pattern in India’s district and statewise.Design/methodology/approachSpatial regression like spatial autoregressivel, SEM, SDM, SDEM and GNS model has been worded out in this. The linear regressions assume the specific assumption about the function form which has origin from Solow growth model. Nonparametric regressions make no assumptions about the functional form of relationships between variables, while random coefficients models extend this approach by allowing the coefficients to vary across groups, capturing heterogeneity at the individual level. Geographically weighted regression (GWR) combines these concepts by incorporating spatial variation, enabling the analysis of how relationships between variables differ across geographic locations. To address spatial heterogeneity, this paper has used GWR, which integrates these ideas by accounting for spatial variation. This approach enables the analysis of how relationships between variables differ across geographic locations, making it particularly useful for studying both conditional and unconditional convergence.FindingsThis study offers valuable insights into the dynamics of convergence and divergence at both state and district levels in India. It highlights the role of spatial factors and the impact of various variables on the convergence process, shedding light on the nuanced patterns of economic growth and development within the country.Research limitations/implicationsThe significant inequality among Indian districts, alongside evidence of conditional convergence, underscores the urgent need for targeted policy interventions to boost growth in lagging regions. Central initiatives like the “One District, One Product” and the “Aspirational District Program” are vital for addressing disparities. Still, their success depends on effective implementation, monitoring and evaluation at the district level. Complementing these efforts, this paper’s analysis of spatial income convergence highlights the importance of developing alternative growth hubs, such as those under the Counter Magnet Area plan, to ease migration pressure, decongest core regions like Delhi and promote balanced development through improved infrastructure and local economic opportunities in peripheral areas.Social implicationsThe pronounced inequality among Indian districts, coupled with evidence of conditional convergence, highlights the pressing need for tailored policy measures to stimulate growth in underperforming regions. National initiatives like “One District, One Product” and the “Aspirational District Program” play a crucial role in reducing disparities. However, their effectiveness hinges on robust implementation, thorough monitoring and periodic evaluation at the district level. In addition, this paper’s study on spatial income convergence emphasizes the necessity of fostering alternative growth centers, such as those proposed under the Counter Magnet Area plan, to alleviate migration pressures, reduce congestion in central regions like Delhi, and encourage balanced development through enhanced infrastructure and localized economic opportunities in outlying areas.Originality/valueThis paper’s analysis reveals positive spatial autocorrelation for certain variables, such as gross fixed capital formation, initial GDP levels, health index, per capita power consumption and literacy rate, indicating spatial clustering and the influence of spatial factors. This paper explores both unconditional and conditional convergence, with results suggesting that while income convergence trends are evident, they vary significantly across regions. The financial inclusive index and good health index play crucial roles in driving growth, with positive coefficients indicating their positive influence.

Keywords: Unconditional convergence; Conditional convergence; Speed of unconditional convergence; Spatial models; Spatial convergence model; Spatial Mankiw–Romer model (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eme:igdrpp:igdr-11-2024-0196

DOI: 10.1108/IGDR-11-2024-0196

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